On Convergence of Minimization Methods: Attraction, Repulsion and Selection

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dc.contributor.authorZhang, Yin
dc.contributor.authorTapia, Richard
dc.contributor.authorVelazquez, Leticia
dc.date.accessioned2018-06-18T17:47:33Z
dc.date.available2018-06-18T17:47:33Z
dc.date.issued1999-03
dc.date.noteMarch 1999 (Revised August 1999)
dc.description.abstractIn this paper, we introduce a rather straightforward but fundamental observation concerning the convergence of the general iteration process. x^(k+1) = x^k - alpha(x^k) [B(x^k)]^(-1) gradĀ­f(x^k) for minimizing a function f(x). We give necessary and sufficient conditions for a stationary point of f(x) to be a point of strong attraction of the iteration process. We will discuss various ramifications of this fundamental result, particularly for nonlinear least squares problems.
dc.format.extent18 pp
dc.identifier.citationZhang, Yin, Tapia, Richard and Velazquez, Leticia. "On Convergence of Minimization Methods: Attraction, Repulsion and Selection." (1999) <a href="https://hdl.handle.net/1911/101917">https://hdl.handle.net/1911/101917</a>.
dc.identifier.digitalTR99-12
dc.identifier.urihttps://hdl.handle.net/1911/101917
dc.language.isoeng
dc.titleOn Convergence of Minimization Methods: Attraction, Repulsion and Selection
dc.typeTechnical report
dc.type.dcmiText
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